The Layer Between Agents And Your Systems.

Model Context Protocol is an open standard for exposing tools and data to AI agents through a consistent interface. It matters because it decouples your integration work from any single vendor: build one MCP server for your order system and every MCP-compatible agent or client can use it, rather than writing a new connector for each assistant your teams adopt. That is a meaningful reduction in long-term integration maintenance.

If you have one agent talking to a handful of internal endpoints, direct function calling is simpler and we will tell you to use it. MCP earns its keep when the same capability needs to be reachable from several agents or clients, when you want a clean boundary between the agent team and the system owners, or when you expect the agent landscape you use to keep changing. The honest answer depends on your scale.

The tool layer is the enforcement point, not the prompt. Destructive operations either do not exist as tools, or they require an approval token the agent cannot mint itself. Limits — spend caps, row-count ceilings, rate limits — are checked server-side on every call. The design assumption is that the model will eventually be wrong or manipulated, so the tool layer has to hold on its own.

Yes, and it is a large part of this work. We build a bridging service that speaks the legacy protocol — SOAP, fixed-width files, database-level integration, mainframe transaction interfaces — and exposes a clean, typed tool to the agent. We avoid UI scraping wherever an alternative exists, because scraped integrations break on cosmetic changes and fail silently.

It is a real attack path: content returned by a tool — a support ticket, a web page, a document — can contain instructions aimed at the model. We treat all tool output as untrusted data rather than instructions, isolate it structurally in the context, and put the actual authority in the tool layer where injected text has no effect. An agent that is talked into calling a destructive tool still cannot call one that does not exist for it.

Three to eight weeks for a coherent set of tools around one domain, with the variance driven almost entirely by the state of the underlying systems. A documented REST API with a sandbox is fast; an undocumented internal service where the original author has left is not. We assess this in week one so the estimate reflects your systems rather than an average.